Ant-Colony-Based Geographic and Energy Balance Routing for Sensor Networks

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Recent advances in wireless sensor networks (WSNs) now witness the increased interest in the potential use in applications. Sensors are expected to be remotely deployed in large numbers and operate autonomously in unattended environments. In this paper, Ant-colony-based Geographic and Energy Balance Routing (AGEBR) was utilized to establish wireless sensor networks which had survival cycles. This Ant-colony-based routing can modify the geographic information based on the distribution characteristics of the ant colony algorithm, and finally achieve fast convergence and high efficiency. As a result, the sensor networks which did not have lasting energy supplement or only had limited energy complement could last a longer period without losing their sensing ability. Experimental results demonstrated that the proposed approach can keep balance of the network energy consumption, prolong the network lifetime, and enhance the successful sending rate without congestion.

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1049-1055

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December 2012

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Y. MJennifer, M. Biswanath, G. Dipak, Wireless sensor network survey, Computer Networks: The International Journal of Computer and Telecommunications Networking, v. 52 (12), 2292-2330, August, (2008).

DOI: 10.1016/j.comnet.2008.04.002

Google Scholar

[2] H.O. Tan, I. Korpeoglu, I. Stojmenovic, Computing Localized Power-Efficient Data Aggregation Trees for Sensor Networks, Parallel and Distributed Systems, Volume: 22, Issue: 3, 489 – 500, (2011).

DOI: 10.1109/tpds.2010.68

Google Scholar

[3] T. Wei, S. Yu, H. Wei, A dynamic voltage scaling algorithm for wireless sensor networks, Advanced Computer Theory and Engineering (ICACTE). Volume: 1, Page(s): 554 - 557, (2010).

DOI: 10.1109/icacte.2010.5578956

Google Scholar

[4] T.T. Long, J. Levendovszky, A Novel Reliability Based Routing Protocol for Power Aware Communications in Wireless Sensor Networks. Wireless Communications and Networking Conference (WCNC 2009), IEEE. Page(s): 1 – 6, (2009).

DOI: 10.1109/wcnc.2009.4917530

Google Scholar

[5] Woo A, Tong T, and Culler D. Taming the underlying challenges of reliable multihop routing in sensor networks. Proceedings First International Conference on Embedded Networked Sensor Systems, 14-27, ACM, (2003).

DOI: 10.1145/958491.958494

Google Scholar

[6] H.O. Tan, I. Korpeoglu, I. Stojmenovic: Computing Localized Power-Efficient Data Aggregation Trees for Sensor Networks. Parallel and Distributed Systems 22, 489—500 (2011).

DOI: 10.1109/tpds.2010.68

Google Scholar

[7] T.M. Wei, S.J. Yu, H.L. Wei: A Dynamic Voltage Scaling Algorithm for Wireless Sensor Networks. Advanced Computer Theory and Engineering (ICACTE) 1, 554—557 (2010).

DOI: 10.1109/icacte.2010.5578956

Google Scholar

[8] T. Long, J. Levendovszky: A Novel Reliability Based Routing Protocol for Power Aware Communications in Wireless Sensor Networks. In: IEEE Wireless Communications and Networking Conference, p.1—6. (2009).

DOI: 10.1109/wcnc.2009.4917530

Google Scholar

[9] M. Zuniga,B. Krishnamachari, Analyzing the transitional region in low power wireless links, in IEEE Secon'04, (2004).

DOI: 10.1109/sahcn.2004.1381954

Google Scholar

[10] K. Seada, M. Zuniga, A. Helmy, and B. Krishnamachari, Energy efficient forwarding strategies for geographic routing in wireless sensor networks, in ACM Sensys'04, Baltimore, MD, Nov. (2004).

DOI: 10.1145/1031495.1031509

Google Scholar

[11] A. Kansal, M. B. Srivastava, An environmental energy harvesting framework for sensor networks, International symposium on Low power electronics and design, 481–486, (2003).

DOI: 10.1145/871506.871624

Google Scholar